
#Libraries to be installed
##!pip -q is to give less output
!pip -q install sodapy
!pip -q install seaborn
!pip -q install pandas
!pip -q install matplotlib
!pip -q install numpy
!pip -q install nbconvert
!pip -q install keyboard
!pip -q install geopandas
!pip -q install sklearn
!pip -q install folium


#load libraries
import os
import time
import keyboard

from datetime import datetime
import numpy as np
import pandas as pd
from sodapy import Socrata
import seaborn as sns

from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA

import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.cm as cm
import matplotlib.colors as colors
from matplotlib import style
style.use('ggplot')

import plotly.graph_objs as go
import plotly.express as px

import warnings
warnings.filterwarnings('ignore')

import folium
from folium.plugins import MarkerCluster

#Socrata client connection
client = Socrata('data.melbourne.vic.gov.au', '9UAGAvkem9fqXDhBLElapjazL', None)


#Pedestrian sensor location data
sensor_data_id = "h57g-5234"
results = client.get(sensor_data_id)
sensor_location = pd.DataFrame.from_records(results)
#sensor_location.head(5)

sensor_location[['latitude', 'longitude']] = sensor_location[['latitude', 'longitude']].astype(float)
sensor_location = sensor_location.drop('location',axis=1)
sensor_location['lat'] = sensor_location['latitude'].apply(lambda x: float(x))
sensor_location['lon'] = sensor_location['longitude'].apply(lambda x: float(x))

#View sensor data
sensor_location.info()
sensor_location.head(5)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 91 entries, 0 to 90
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   sensor_id           91 non-null     object 
 1   sensor_description  91 non-null     object 
 2   sensor_name         91 non-null     object 
 3   installation_date   91 non-null     object 
 4   status              91 non-null     object 
 5   note                10 non-null     object 
 6   latitude            91 non-null     float64
 7   longitude           91 non-null     float64
 8   direction_1         78 non-null     object 
 9   direction_2         78 non-null     object 
 10  lat                 91 non-null     float64
 11  lon                 91 non-null     float64
dtypes: float64(4), object(8)
memory usage: 8.7+ KB


#Pedestrian foot count data 
#Uncomment the below to open data source, download sensor data, and store it as a csv locally.
#sensor_data_id = "b2ak-trbp"
#results = client.get(sensor_data_id, limit=7000000)
#ds_traffic = pd.DataFrame.from_records(results)
#ds_traffic.to_csv('sensor_traffic.csv', index=False)

sensor_traffic = pd.read_csv('sensor_traffic.csv')
sensor_traffic.info()

print(f'\nThe shape of dataset is {sensor_traffic.shape}. \n')
print('Below are the first few rows of this dataset: ')
sensor_traffic.head(10)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4463670 entries, 0 to 4463669
Data columns (total 10 columns):
 #   Column         Dtype 
---  ------         ----- 
 0   id             int64 
 1   date_time      object
 2   year           int64 
 3   month          object
 4   mdate          int64 
 5   day            object
 6   time           int64 
 7   sensor_id      int64 
 8   sensor_name    object
 9   hourly_counts  int64 
dtypes: int64(6), object(4)
memory usage: 340.6+ MB

The shape of dataset is (4463670, 10). 

Below are the first few rows of this dataset:


#Add date column
sensor_traffic['date'] = pd.to_datetime(sensor_traffic.date_time).dt.date

#Add day of week column
sensor_traffic['dow'] = pd.to_datetime(sensor_traffic.date_time).dt.day_of_week

#convert sensor_id to integer
sensor_traffic['sensor_id']=sensor_traffic['sensor_id'].astype(int)
sensor_location['sensor_id']=sensor_location['sensor_id'].astype(int)

# Mesh pedestrian sensor location and foot traffic datasets
sensor_ds = pd.merge(sensor_traffic, sensor_location, on='sensor_id')

#Simply using the year to differentiate all the years prior to 2020 as before Covid, and post 2019 Covid traffic to now
sensor_ds['pre2020_hourly_counts'] = np.where(sensor_ds['year']<2020,sensor_ds['hourly_counts'] , 0)
sensor_ds['post2019_hourly_counts'] = np.where(sensor_ds['year']>2019,sensor_ds['hourly_counts'] , 0)

#Add column for day or night traffic
#Add column for day (5am to 5pm) or night (6pm to 4am) traffic
sensor_ds['day_counts']   = np.where(((sensor_ds['time']>4)  & (sensor_ds['time']<18)),sensor_ds['hourly_counts'] , 0)
sensor_ds['night_counts'] = np.where(sensor_ds['day_counts']==0,sensor_ds['hourly_counts'], 0)

sensor_ds.describe()


flag_value=0
df_day=sensor_ds.query("day_counts > @flag_value")
print('Day info\n', df_day.info(),'\n')

df_night=sensor_ds.query("day_counts == @flag_value")
print('Night info \n',df_night.info(),'\n')

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2402520 entries, 0 to 4463663
Data columns (total 27 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   id                      int64  
 1   date_time               object 
 2   year                    int64  
 3   month                   object 
 4   mdate                   int64  
 5   day                     object 
 6   time                    int64  
 7   sensor_id               int32  
 8   sensor_name_x           object 
 9   hourly_counts           int64  
 10  date                    object 
 11  dow                     int64  
 12  sensor_description      object 
 13  sensor_name_y           object 
 14  installation_date       object 
 15  status                  object 
 16  note                    object 
 17  latitude                float64
 18  longitude               float64
 19  direction_1             object 
 20  direction_2             object 
 21  lat                     float64
 22  lon                     float64
 23  pre2020_hourly_counts   int64  
 24  post2019_hourly_counts  int64  
 25  day_counts              int64  
 26  night_counts            int64  
dtypes: float64(4), int32(1), int64(10), object(12)
memory usage: 504.1+ MB
Day info
 None 

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2061150 entries, 1 to 4463669
Data columns (total 27 columns):
 #   Column                  Dtype  
---  ------                  -----  
 0   id                      int64  
 1   date_time               object 
 2   year                    int64  
 3   month                   object 
 4   mdate                   int64  
 5   day                     object 
 6   time                    int64  
 7   sensor_id               int32  
 8   sensor_name_x           object 
 9   hourly_counts           int64  
 10  date                    object 
 11  dow                     int64  
 12  sensor_description      object 
 13  sensor_name_y           object 
 14  installation_date       object 
 15  status                  object 
 16  note                    object 
 17  latitude                float64
 18  longitude               float64
 19  direction_1             object 
 20  direction_2             object 
 21  lat                     float64
 22  lon                     float64
 23  pre2020_hourly_counts   int64  
 24  post2019_hourly_counts  int64  
 25  day_counts              int64  
 26  night_counts            int64  
dtypes: float64(4), int32(1), int64(10), object(12)
memory usage: 432.4+ MB
Night info 
 None


#split dataset to map difference between time before covid-19 and time after covid-19
df = df_day
print(df_day.head(2))

df_daypft_beforecovid = df.loc[df['year']<2020]
df_daypft_aftercovid = df.loc[df['year']>2019]

#get average hourly count for each sensor during the selected period of time
df_daypft_beforecovid_avg = df_daypft_beforecovid[['sensor_id','sensor_description','lat','lon','hourly_counts']]
df_daypft_beforecovid_avg = df_daypft_beforecovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'hourly_counts': 'mean'})
df_daypft_aftercovid_avg = df_daypft_aftercovid[['sensor_id','sensor_description','lat','lon','hourly_counts']]
df_daypft_aftercovid_avg = df_daypft_aftercovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'hourly_counts': 'mean'})

         id                date_time  year     month  mdate       day  time  \
0   2887628  2019-11-01T17:00:00.000  2019  November      1    Friday    17   
12  2888288  2019-11-02T05:00:00.000  2019  November      2  Saturday     5   

    sensor_id         sensor_name_x  hourly_counts  ...  latitude   longitude  \
0          34  Flinders St-Spark La            300  ... -37.81538  144.974151   
12         34  Flinders St-Spark La              7  ... -37.81538  144.974151   

   direction_1 direction_2       lat         lon pre2020_hourly_counts  \
0         East        West -37.81538  144.974151                   300   
12        East        West -37.81538  144.974151                     7   

    post2019_hourly_counts  day_counts night_counts  
0                        0         300            0  
12                       0           7            0  

[2 rows x 27 columns]


#split dataset to see difference between time before covid-19 and time after covid-19
df = df_night
print(df.head(2))

df_night_beforecovid = df.loc[df['year']<2020]
df_night_aftercovid = df.loc[df['year']>2019]

#get average hourly count for each sensor during the selected period of time
df_night_beforecovid_avg = df_night_beforecovid[['sensor_id','sensor_description','lat','lon','pre2020_hourly_counts']]
df_night_beforecovid_avg = df_night_beforecovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'pre2020_hourly_counts': 'mean'})
df_night_aftercovid_avg = df_night_aftercovid[['sensor_id','sensor_description','lat','lon','post2019_hourly_counts']]
df_night_aftercovid_avg = df_night_aftercovid_avg.groupby(['sensor_id','sensor_description','lat','lon',],as_index=False).agg({'post2019_hourly_counts': 'mean'})

        id                date_time  year     month  mdate     day  time  \
1  2887683  2019-11-01T18:00:00.000  2019  November      1  Friday    18   
2  2887738  2019-11-01T19:00:00.000  2019  November      1  Friday    19   

   sensor_id         sensor_name_x  hourly_counts  ...  latitude   longitude  \
1         34  Flinders St-Spark La            240  ... -37.81538  144.974151   
2         34  Flinders St-Spark La            158  ... -37.81538  144.974151   

  direction_1 direction_2       lat         lon pre2020_hourly_counts  \
1        East        West -37.81538  144.974151                   240   
2        East        West -37.81538  144.974151                   158   

   post2019_hourly_counts  day_counts night_counts  
1                       0           0          240  
2                       0           0          158  

[2 rows x 27 columns]


#examine pre Covid and post 2019 foot traffic
ds = pd.DataFrame(sensor_ds.groupby(["time"])["pre2020_hourly_counts","post2019_hourly_counts"].mean())
df = ds.sort_values(by=['time'])
axs = df.plot.line(figsize=(20, 6), color=["teal","orange"])
axs.set_title('Foot Traffic by Time', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()


#distribution by traffic, by day
pivot = pd.DataFrame(pd.pivot_table(sensor_ds, values=['day_counts','night_counts'], index=['year'], aggfunc=np.mean))
rs = pivot.sort_values(by='year', ascending = False)
axs = rs.plot.line(figsize=(12, 5), color=['orange','navy'], legend=True);

axs.set_title('Foot Traffic by Year by day and night', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()


#examine pre Covid and post 2019 foot traffic
ds = pd.DataFrame(sensor_ds.groupby(["dow","day"])["pre2020_hourly_counts","post2019_hourly_counts"].mean())
df = ds.sort_values(by=['dow'])
axs = df.plot.bar(figsize=(12, 4), color=["teal","orange"])
axs.set_title('Foot Traffic by Day of Week - All Hours', size=20)
axs.set_ylabel('Average hourly counts', size=14)
plt.show()


#distribution by traffic, by day
pivot = pd.pivot_table(sensor_ds, values='day_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['day_counts'].nlargest(n=20)
pivot_ds.plot.bar(figsize=(12, 5), color='orange', legend=True);

#by night
pivot = pd.pivot_table(sensor_ds, values='night_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['night_counts'].nlargest(n=20)
axs = pivot_ds.plot.bar(figsize=(12, 5), color='navy', legend=True);

axs.set_title('Top 20: Foot Traffic by location by day and night', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()


#distribution by traffic, by day
pivot = pd.pivot_table(df_day, values='pre2020_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['pre2020_hourly_counts'].nlargest(n=20)
pivot_ds.plot.line(figsize=(12, 5), color='teal', legend=True, rot=90);

#by night
pivot = pd.pivot_table(df_day, values='post2019_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['post2019_hourly_counts'].nlargest(n=20)
axs = pivot_ds.plot.bar(figsize=(12, 5), color='orange', legend=True, rot=90);

axs.set_title('Top 20: Foot Traffic by location by day', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()


#Visualise day data
m = folium.Map(location=[-37.8167, 144.967], zoom_start=15) # tiles='Stamen Toner'
locations = []
for i in range(len(df_daypft_beforecovid_avg)):
    row =df_daypft_beforecovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='day traffic before covid-19',
  locations=locations,
  overlay=True,
  control=True,
  color='red',
  show=False # this removes from automatic selection in display - need to select to show data points
  )
marker_cluster.add_to(m)

#next layer
locations = []
for i in range(len(df_daypft_aftercovid_avg)):
    row =df_daypft_aftercovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='day traffic after covid-19',
  locations=locations,
  overlay=True,
  control=True,
  ) 
marker_cluster.add_to(m)
folium.LayerControl().add_to(m)
m


#identify class and features
use_list = ["sensor_id","lat","lon","hourly_counts"]
loc_day = pd.DataFrame(df_day[use_list])
print(loc_day.head(5))

codes = loc_day[['sensor_id']] #class
loc_day.drop('sensor_id', axis=1, inplace=True)

loc_day.head(5)

    sensor_id       lat         lon  hourly_counts
0          34 -37.81538  144.974151            300
12         34 -37.81538  144.974151              7
13         34 -37.81538  144.974151             16
14         34 -37.81538  144.974151             37
15         34 -37.81538  144.974151             36


#examine data
loc_day.describe()


# identify optimal clusters 
features_list = ["lat","lon","hourly_counts"]
df_features = pd.DataFrame(loc_day[features_list])

#scale the features
mms = MinMaxScaler()
mms.fit(df_features) #day hours
data_transformed = mms.transform(df_features)

#data_transformed

# for each k value calculate sum of squared distances to the nearest cluster centre
Sum_of_squared_distances = []
K = range(1,10)
for k in K:
    km = KMeans(n_clusters=k)
    km = km.fit(data_transformed)
    Sum_of_squared_distances.append(km.inertia_)
    
#plot results
plt.plot(K, Sum_of_squared_distances, 'g*-')
plt.xlabel('Number of Clusters')
plt.ylabel('Sum of squared distances')
plt.title('Elbow Method')
plt.show()


# run k-means clustering, define centroids
kmeans = KMeans(n_clusters=3, random_state=0).fit(loc_day)
centroids = kmeans.cluster_centers_

codes['cluster'] = kmeans.labels_
#codes.head()


df = df_features

#visualise data
x_axis = df['lon']
y_axis = df['lat']
cluster_legend = kmeans.labels_.astype(float)
plt.figure(figsize = (8,5))
sns.scatterplot(x_axis, y_axis, hue=cluster_legend, c=cluster_legend, palette='deep', s=60)
plt.title('Clustering Pedestrian Counts: Day Hours')
plt.show()


#distribution by traffic
#by night before covid
pivot = pd.pivot_table(df_night_beforecovid, values='pre2020_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['pre2020_hourly_counts'].nlargest(n=20)
pivot_ds.plot.bar(figsize=(12, 5), color='teal', legend=True);

#by night after Covid
pivot = pd.pivot_table(df_night_aftercovid, values='post2019_hourly_counts', index=['sensor_id','sensor_description'], aggfunc=np.mean)
pivot_ds = pivot['post2019_hourly_counts'].nlargest(n=20)
axs = pivot_ds.plot.bar(figsize=(12, 5), color='navy', legend=True);

axs.set_title('Top 20: Foot Traffic by location by night, before and after Covid', size=20)
axs.set_ylabel('Average counts', size=14)
plt.show()


#Visualise night data
m = folium.Map(location=[-37.8167, 144.967], zoom_start=15)
locations = []
for i in range(len(df_night_beforecovid_avg)):
    row =df_night_beforecovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['pre2020_hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='Night traffic before Covid-19',
  locations=locations,
  overlay=True,
  control=True,
  show=False # this removes from automatic selection in display - need to select to show data points
  )
marker_cluster.add_to(m)
locations = []
for i in range(len(df_night_aftercovid_avg)):
    row =df_night_aftercovid_avg.iloc[i]
    location = [(row.lat,row.lon)]*int(row['post2019_hourly_counts'])
    locations += location
marker_cluster  = MarkerCluster(
  name='Night traffic after Covid-19',
  locations=locations,
  overlay=True,
  control=True,
  ) 
marker_cluster.add_to(m)
folium.LayerControl().add_to(m)
m


#identify class and features
use_list = ["sensor_id","lat","lon","hourly_counts"]
loc_night = pd.DataFrame(df_night[use_list])
#print(loc_night.head(5))

codes = loc_night[['sensor_id']] #class
loc_night.drop('sensor_id', axis=1, inplace=True)

loc_night.head(5)


# identify optimal clusters - these may be the same or different to day
features_list = ["lat","lon","hourly_counts"]
df_features = pd.DataFrame(loc_night[features_list])

#scale the features
mms = MinMaxScaler()
mms.fit(df_features) #day hours
data_transformed = mms.transform(df_features)

#data_transformed

# for each k value calculate sum of squared distances to the nearest cluster centre
Sum_of_squared_distances = []
K = range(1,10)
for k in K:
    km = KMeans(n_clusters=k)
    km = km.fit(data_transformed)
    Sum_of_squared_distances.append(km.inertia_)
    
#plot results
plt.plot(K, Sum_of_squared_distances, 'g*-')
plt.xlabel('Number of Clusters')
plt.ylabel('Sum of squared distances')
plt.title('Elbow Method')
plt.show()


# run k-means clustering, define centroids
kmeans = KMeans(n_clusters=3, random_state=0).fit(loc_night)
centroids = kmeans.cluster_centers_

codes['cluster'] = kmeans.labels_
#codes.head()


df = df_features

#visualise data
x_axis = df['lon']
y_axis = df['lat']
cluster_legend = kmeans.labels_.astype(int)
plt.figure(figsize = (8,5))
axs = sns.scatterplot(x_axis, y_axis, hue=cluster_legend, c=cluster_legend, palette='deep', s=60)
plt.title('Clustering Pedestrian Counts: Night Hours')
plt.show()


#save notebook, required so that step to convert to html, writes latest results to file
# may need to adapt for other OS, this is for Windows
keyboard.press_and_release('ctrl+s')

!jupyter nbconvert  usecase_evaluate_business_location_using_pedestrian_traffic_day_night.ipynb --to html

	sensor_id	sensor_description	sensor_name	installation_date	status	note	latitude	longitude	direction_1	direction_2	lat	lon
0	16	Australia on Collins	Col270_T	2009-03-30T00:00:00.000	R	Device moved to location ID 53 (22/09/2015)	-37.815734	144.965210	NaN	NaN	-37.815734	144.965210
1	50	Faraday St-Lygon St (West)	Lyg309_T	2017-11-30T00:00:00.000	A	NaN	-37.798082	144.967210	South	North	-37.798082	144.967210
2	73	Bourke St - Spencer St (South)	Bou655_T	2020-10-02T00:00:00.000	I	NaN	-37.816957	144.954154	East	West	-37.816957	144.954154
3	66	State Library - New	QVN_T	2020-04-06T00:00:00.000	A	NaN	-37.810578	144.964443	South	North	-37.810578	144.964443
4	59	Building 80 RMIT	RMIT_T	2019-02-13T00:00:00.000	A	NaN	-37.808256	144.963049	South	North	-37.808256	144.963049

	id	year	mdate	time	sensor_id	hourly_counts	dow	latitude	longitude	lat	lon	pre2020_hourly_counts	post2019_hourly_counts	day_counts	night_counts
count	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06	4.463670e+06
mean	2.232320e+06	2.017230e+03	1.574741e+01	1.146917e+01	2.700226e+01	4.951352e+02	2.999995e+00	-3.781345e+01	1.449621e+02	-3.781345e+01	1.449621e+02	4.074119e+02	8.772332e+01	3.594659e+02	1.356693e+02
std	1.289316e+06	3.551188e+00	8.800020e+00	6.938594e+00	1.951539e+01	7.509822e+02	2.000015e+00	6.187582e-03	8.531568e-03	6.187582e-03	8.531568e-03	7.443612e+02	2.852713e+02	7.131327e+02	3.910920e+02
min	1.000000e+00	2.009000e+03	1.000000e+00	0.000000e+00	1.000000e+00	0.000000e+00	0.000000e+00	-3.782402e+01	1.449297e+02	-3.782402e+01	1.449297e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
25%	1.115918e+06	2.015000e+03	8.000000e+00	5.000000e+00	1.100000e+01	4.300000e+01	1.000000e+00	-3.781874e+01	1.449587e+02	-3.781874e+01	1.449587e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
50%	2.231836e+06	2.018000e+03	1.600000e+01	1.100000e+01	2.300000e+01	1.780000e+02	3.000000e+00	-3.781381e+01	1.449651e+02	-3.781381e+01	1.449651e+02	6.200000e+01	0.000000e+00	2.400000e+01	0.000000e+00
75%	3.347753e+06	2.020000e+03	2.300000e+01	1.700000e+01	4.100000e+01	6.050000e+02	5.000000e+00	-3.781102e+01	1.449669e+02	-3.781102e+01	1.449669e+02	4.530000e+02	2.400000e+01	3.520000e+02	5.900000e+01
max	4.469141e+06	2.022000e+03	3.100000e+01	2.300000e+01	8.700000e+01	1.597900e+04	6.000000e+00	-3.779432e+01	1.449747e+02	-3.779432e+01	1.449747e+02	1.597900e+04	1.443700e+04	1.161200e+04	1.597900e+04

	lat	lon	hourly_counts
count	2.402520e+06	2.402520e+06	2.402520e+06
mean	-3.781345e+01	1.449621e+02	6.678559e+02
std	6.182429e-03	8.520379e-03	8.595908e+02
min	-3.782402e+01	1.449297e+02	1.000000e+00
25%	-3.781874e+01	1.449587e+02	1.050000e+02
50%	-3.781381e+01	1.449651e+02	3.040000e+02
75%	-3.781102e+01	1.449669e+02	8.810000e+02
max	-3.779432e+01	1.449747e+02	1.161200e+04

Load Pedestrian sensor location data¶

Load Pedestrian monthly hourly count data¶

Merge Pedestrian sensor location and traffic data¶

Separate day and night data sets¶

Compare day traffic before Covid and now¶

Clustering: Day Traffic¶

The exploratory data analysis on day and night showing initial findings is available, please refer to eda compare pedestrian traffic day night ¶

Compare night traffic before Covid and now¶

Clustering: Night Traffic¶

For night traffic we will try using 3 clusters¶

	id	date_time	year	month	mdate	day	time	sensor_id	sensor_name	hourly_counts
0	2887628	2019-11-01T17:00:00.000	2019	November	1	Friday	17	34	Flinders St-Spark La	300
1	2887629	2019-11-01T17:00:00.000	2019	November	1	Friday	17	39	Alfred Place	604
2	2887630	2019-11-01T17:00:00.000	2019	November	1	Friday	17	37	Lygon St (East)	216
3	2887631	2019-11-01T17:00:00.000	2019	November	1	Friday	17	40	Lonsdale St-Spring St (West)	627
4	2887632	2019-11-01T17:00:00.000	2019	November	1	Friday	17	36	Queen St (West)	774
5	2887633	2019-11-01T17:00:00.000	2019	November	1	Friday	17	29	St Kilda Rd-Alexandra Gardens	644
6	2887634	2019-11-01T17:00:00.000	2019	November	1	Friday	17	42	Grattan St-Swanston St (West)	453
7	2887635	2019-11-01T17:00:00.000	2019	November	1	Friday	17	43	Monash Rd-Swanston St (West)	387
8	2887636	2019-11-01T17:00:00.000	2019	November	1	Friday	17	44	Tin Alley-Swanston St (West)	27
9	2887637	2019-11-01T17:00:00.000	2019	November	1	Friday	17	35	Southbank	2691

	lat	lon	hourly_counts
0	-37.81538	144.974151	300
12	-37.81538	144.974151	7
13	-37.81538	144.974151	16
14	-37.81538	144.974151	37
15	-37.81538	144.974151	36

Load Pedestrian sensor location data¶

Load Pedestrian monthly hourly count data¶

Merge Pedestrian sensor location and traffic data¶

Separate day and night data sets¶

Compare day traffic before Covid and now¶

Clustering: Day Traffic¶

The exploratory data analysis on day and night showing initial findings is available, please refer to eda compare pedestrian traffic day night¶

Compare night traffic before Covid and now¶

Clustering: Night Traffic¶

For night traffic we will try using 3 clusters¶

The exploratory data analysis on day and night showing initial findings is available, please refer to eda compare pedestrian traffic day night ¶